In recent years, the use of digital imaging, where an image is represented as an array of digital values, has increased dramatically both in industrial implementations and in everyday popular devices ranging from simple cameras to television broadcasting. Although the technology of digital imaging has been known in the art for a long time, major technological advancements in the art have made digital images easier to process, store and transmit.
The method for using hardware and/or software to automatically detect and classify objects in images is referred to hereinafter as object recognition, where an object may be any physical or non physical entity with a certain texture in the image. A physical entity may be understood as an object that has a measurable volume such as a ball, building, vehicle, animal, etc. whereas the non physical entities such as patterns, colors, or sequences do not have a measurable volume. The problem of automatic object recognition is coping with many variations that a single object may have. For example, a vehicle may have different visual variations. It may be imaged from different angles, where in each angle the vehicle appears differently. The car may also be imaged in different sizes or in different colors. Furthermore, many models of cars exist with major differences in appearance ranging from different outlines of the silhouette to different details of headlights, grill, and windows. Another example is the human image of different people which varies in sizes, shapes, shades, angles, and positions, a fact that increases the difficulty of object recognition even further. The human image may belong to an old man or a young boy, short or tall, dark or bright, etc. The image may show a human standing, sitting or running where different details like leg position vary drastically.
Another problem of object recognition arises from the quality of the acquired image. Some of the digital images are blurred or cluttered in a way that the contrast between objects in the image is not easily distinguishable. A good example may be a photograph taken by a low resolution camera, or in bad lighting conditions. The same problem may arise for an object pictured in an environment having similar color and shade such as a white plate on a white table cloth. Another example may be found in the area of medical photography, where the low resolution of an X-ray image is a result of limited radiation.
Many computer-based methods for object recognition use databases for profiling objects, and statistical modeling to represent the variability of the objects, whereas the statistical modeling helps describe a quantity that is not fixed or deterministic. These methods use the versatility of statistics to enhance the profile of an image for improving the chances of recognizing it in different positions. However, these methods rely on strong computational capabilities of the performing computer as the statistical calculations involved are complicated and consume many resources.
U.S. Pat. No. 6,829,384 describes a method for detecting the presence of 3-d objects in a 2-d image. The method utilizes a pre-selected number of detectors that are trained on sample images prior to operating on a given input image. The method discloses an exhaustive object search of different positions and scales in accordance with a detection strategy. The method described within uses quantized wavelet coefficients at different locations on the input image together with using pre-computed likelihood table to determine object presence. The publication discloses the statistical model the method uses, which relies on complex calculations and vast computational capabilities.
U.S. Pat. No. 6,421,463 describes a trainable system for detecting objects in images. The system is capable of detecting objects with variability in size, shape, color and texture without relying on any a priori models. The invention utilizes a wavelet template that defines the shape of an object in terms of a subset of wavelet coefficients of the image. A classifier used with an image database for object recognition, is described in the patent. The method of the invention detects an object by iteratively resizing the image to achieve multi-scale detection. Nevertheless, the system described in the patent lacks the ability to recognize objects in low resolution images.
It is an object of the present invention to provide a method for automatic detection and classification of objects in low resolution images, including objects which may be undetectable by a human eye.
It is another object of the present invention to provide a method for automatic detection and classification of objects in low resolution images in such scenarios where human presence may be dangerous and unadvisable.
It is still another object of the present invention to provide a method for automatic detection and classification of objects in low resolution images, in order to replace humans and achieve higher efficiency in the process.
It is still another object of the present invention to provide a method for a trainable system capable of learning object characteristics from images.
It is still another object of the present invention to provide a method that utilizes a fast algorithm for receiving results in real time.
It is still another object of the present invention to provide a method capable of detecting and classifying objects in multi dimensional environments.
Other objects and advantages of the invention will become apparent as the description proceeds.